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pro vyhledávání: '"Zeng, Chaolv"'
Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. However, a critical aspect often overlooked by many existing methods is th
Externí odkaz:
http://arxiv.org/abs/2412.01557
Recent advancements in multivariate time series forecasting have been propelled by Linear-based, Transformer-based, and Convolution-based models, with Transformer-based architectures gaining prominence for their efficacy in temporal and cross-channel
Externí odkaz:
http://arxiv.org/abs/2406.05316
With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original
Externí odkaz:
http://arxiv.org/abs/2403.07294